diff --git a/TTS/tts/models/tortoise.py b/TTS/tts/models/tortoise.py
index fc0e49f7..2b1ab14f 100644
--- a/TTS/tts/models/tortoise.py
+++ b/TTS/tts/models/tortoise.py
@@ -37,6 +37,20 @@ def pad_or_truncate(t, length):
     return tp
 
 
+def deterministic_state(seed=None):
+    """
+    Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
+    reproduced.
+    """
+    seed = int(time()) if seed is None else seed
+    torch.manual_seed(seed)
+    random.seed(seed)
+    # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
+    # torch.use_deterministic_algorithms(True)
+
+    return seed
+
+
 def load_discrete_vocoder_diffuser(
     trained_diffusion_steps=4000,
     desired_diffusion_steps=200,
@@ -93,15 +107,13 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
                 "try breaking up your input text."
             )
         return codes
-    else:
-        codes[stop_token_indices] = 83
+    codes[stop_token_indices] = 83
     stm = stop_token_indices.min().item()
     codes[stm:] = 83
     if stm - 3 < codes.shape[0]:
         codes[-3] = 45
         codes[-2] = 45
         codes[-1] = 248
-
     return codes
 
 
@@ -170,13 +182,14 @@ def pick_best_batch_size_for_gpu():
     if torch.cuda.is_available():
         _, available = torch.cuda.mem_get_info()
         availableGb = available / (1024**3)
+        batch_size = 1
         if availableGb > 14:
-            return 16
+            batch_size = 16
         elif availableGb > 10:
-            return 8
+            batch_size = 8
         elif availableGb > 7:
-            return 4
-    return 1
+            batch_size = 4
+    return batch_size
 
 
 class TextToSpeech:
@@ -184,9 +197,6 @@ class TextToSpeech:
     Main entry point into Tortoise.
     """
 
-    def _config(self):
-        raise RuntimeError("This is depreciated")
-
     def __init__(
         self,
         autoregressive_batch_size=None,
@@ -344,19 +354,6 @@ class TextToSpeech:
         )
         self.cvvp.load_state_dict(torch.load(get_model_path("cvvp.pth", self.models_dir)))
 
-    def deterministic_state(self, seed=None):
-        """
-        Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
-        reproduced.
-        """
-        seed = int(time()) if seed is None else seed
-        torch.manual_seed(seed)
-        random.seed(seed)
-        # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
-        # torch.use_deterministic_algorithms(True)
-
-        return seed
-
     def get_conditioning_latents(
         self,
         voice_samples,
@@ -590,7 +587,7 @@ class TextToSpeech:
         :return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
                  Sample rate is 24kHz.
         """
-        deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
+        deterministic_seed = deterministic_state(seed=use_deterministic_seed)
 
         text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
         text_tokens = F.pad(text_tokens, (0, 1))  # This may not be necessary.